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Questions tagged [machine-learning]

For questions related to machine learning (ML), a type of algorithm that attempts to "learn" how to perform a task without being given an explicit set of rules to follow in order to perform it. Questions on this site relate to the optimization algorithms that underpin ML, applications of ML in practical settings, and other ways that ML can be used as a tool for OR, and vice versa.

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Can ADMM be applied to "latently coupled" variables?

I've been studying a paper where the authors employ the ADMM in a way that has left me somewhat perplexed. The paper focuses on addressing a robust principal component analysis (RPCA) problem, ...
Piko Mone's user avatar
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0 answers
61 views

Workforce scheduling optimization using ML model output

I am working on a workforce scheduling problem where we want to come up with an intelligent way to identify a set of shift candidates that is just enough to cover all labor demand but also flexible ...
SDC's user avatar
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1 vote
0 answers
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What is the suitable optimization method for this case?

What is the best optimization method to solve a large-scale problem (about 300 thousand variables)? The problem is nonlinear, nonconvex, involves only binary variables, and is unconstrained. The ...
Hussein Sharadga's user avatar
2 votes
1 answer
121 views

How to show that minimizing the epsilon-insensitive loss is equivalent to a quadratic program with inequality constraints?

This question is about an optimization problem that arises in support vector regression (SVR). Suppose you have $N$ pairs $(\vec{x}_n, y_n)$ as data and would like to find a vector of weights $\vec w \...
ForceBru's user avatar
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2 votes
0 answers
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Optimization of a noisy loss function

I'm trying to optimize a noisy loss function (experimental) where the absolute value of the gradient changes significantly depending on the direction taken. In other words, some parameters have a ...
Jose Manuel de Frutos's user avatar
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34 views

How to embed an arbitrary graph into (k,d)-kautz space (like multidimensional scaling of non-normed space)

How to embed an arbitrary graph into (k,d)-kautz space (like multidimensional scaling of non-normed space)? See details in the following. Given a graph $G = \{V,E\}$, we have a distance matrix (the ...
Yichuan_Sun's user avatar
3 votes
3 answers
723 views

An efficient method for zoning bins in a warehouse

Let's assume a warehouse with multiple areas, each including either ground or shelf bins. I want to zone bins in this warehouse such that all bins in a zone are as close as possible. Considering that ...
mdslt's user avatar
  • 615
1 vote
0 answers
93 views

Optimization with two constraints using Lagrange multipliers

As a part of an problem where i deploy the EM-algorithm i got stuck with the m-step that can be summarized into the below problem: Consider the following function: $$f(\alpha_{k,l}, \theta_{n, m}) = \...
steward's user avatar
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1 vote
1 answer
172 views

An efficient way to find a postdoc for an operations researcher

Disclaimer: I am not sure if this is the right forum to ask this question. I am looking for a postdoc position in operations research in the US. So far, I have found three ways: INFORMS community (or ...
mdslt's user avatar
  • 615
7 votes
3 answers
403 views

Training ML models to be used as objectives in optimization problems

Suppose that we have data (in my case, from a chemical process) which includes input data $X$ (characteristic of the material to be processed) and decision data $Y$ (decisions taken by operators to ...
Borelian's user avatar
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2 votes
1 answer
130 views

Logic for Re-Labeling Nodes in a Directed Acyclic Graph

We are currently working at the intersection of metaheuristics and machine learning. As part of the scheduling problem that we are trying to solve, we have a project network (directed acylic graph) ...
derhendrik's user avatar
3 votes
0 answers
211 views

What can traditional graph cut methods do well, that deep learning cannot?

I have been fascinated by the rise and fall of graph cut algorithms in recent years, which I described in this question: Was there something specific that caused graph cuts to lose popularity in the ...
Nike Dattani's user avatar
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1 vote
1 answer
266 views

Quality of Solutions from Saddle Points vs. Local Minimums

Can Saddle Points Provide "Better Solutions" to Machine Learning Models Compared to Local Minimums? The solution to a Machine Learning model (i.e. the final model parameters) are selected by ...
stats_noob's user avatar
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14 votes
3 answers
2k views

Why does the design of heuristics require considerable domain knowledge?

I am from a machine learning (ML) background and am interested in how ML is applied to Combinatorial Optimisation. As such, as I have been reading around the area and have come across the statement ...
David's user avatar
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21 votes
3 answers
4k views

Using Neural Networks For Solving Optimization Problems

Recently, I came across the below paper and found it very interesting. Solving Mixed Integer Programs Using Neural Networks; https://arxiv.org/abs/2012.13349 The idea is to use (train with neural ...
alamaranka's user avatar
11 votes
1 answer
189 views

Interplay of OR and Statistics Research

I saw some posts like this so I figured I would start my own. What are some interesting papers in OR that are related to, or even develop, the theory of statistical inference? What are some of the ...
Ariel's user avatar
  • 280
19 votes
1 answer
920 views

Deep Reinforcement Learning for General Purpose Optimization

Recently, I attended a very nice talk given by someone at the place I work about applying Deep Reinforcement Learning (DRL) for a design optimization problem. It was particularly interesting to me ...
chupa_kabra's user avatar
  • 1,485
7 votes
4 answers
671 views

Learning local search operator selection

I'm just reading [1]. The authors use a neural network to solve capacitated vehicle routing problems through iterative generation of tours by solving a price-collecting traveling salesman problem in ...
ktnr's user avatar
  • 491
3 votes
1 answer
221 views

Which method to use to solve this multi-objective conflicting objectives

I have the following multiobjective problem. I need to minimize the user-perceived latency while doing so aggressively minimizing user-perceived latency generates large switching cost (Reconfiguration ...
user1566490's user avatar
6 votes
2 answers
113 views

Optimizing MIP Parameters For Various Data Sets

I have a MIP that runs for several different data sets. For each data set the MIP runs multiple times, once for each time period in the data set, and each time period is independent. I've experimented ...
tjnel's user avatar
  • 163
5 votes
0 answers
88 views

Quantifying Feasibility

I have a scheduling model formulation ( experimental setup) that takes in product states as input (sample points) and checks the model status (response) and returns feasible or infeasible. My plan is ...
Dare Badejo's user avatar
14 votes
4 answers
228 views

How would you characterize "optimization data?"

We often hear that in practice, not enough data of sufficient quality, consistency, recency, etc. is available for feeding into mathematical optimization models. Example: my university wanted to plan/...
Marco Lübbecke's user avatar
3 votes
0 answers
95 views

Combining Machine learning and Operations research on a scheduling problem

I am wondering if there is any attempts of combining OR and ML in the following way. Priority-based rules are widely used in Resource Constrained Project Scheduling Problems. Is there a way to train ...
Best_fit's user avatar
  • 567
7 votes
2 answers
210 views

Interpretability Vs Accuracy in Operations Research and Management Science Community

This question might be somewhat general and not completely relevant to this forum but I think here is the most relevant place to ask the question. Currently, deep learning, RL and generally black-...
Amin's user avatar
  • 2,160
18 votes
3 answers
797 views

Best ways to use machine learning / AI as an OR scientist

I have come across GUROBI's webinar "Mathematical optimization and machine learning". In essence, Mathematical Optimization (MO) and Machine Learning (ML) are different but complementary ...
Kuifje's user avatar
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11 votes
1 answer
245 views

Queuing Theory with Learning Perspective

I am willing to work on queuing models but in classical queuing models, it is assumed the probability distributions of arrival and service are known or at least the rate is known. However, I am ...
Amin's user avatar
  • 2,160
12 votes
1 answer
1k views

Which ML algorithms work by solving constrained optimization problems?

As far as I know, most machine learning algorithms solve unconstrained optimization problems, i.e., if we were to unroll all the neurons into symbolic expressions we would end up with a massive ...
Nikos Kazazakis's user avatar
17 votes
2 answers
873 views

Are there any real-world problems where quadratization helps to solve something that couldn't have been solved without quadratization?

The closest thing I know is the computer vision problem, in which an image is de-blurred and/or de-noised by quadratizing a quartic problem into a quadratic optimization problem (QUBO) and then the ...
Nike Dattani's user avatar
  • 1,278
11 votes
2 answers
538 views

Decoding a Deep Neural Network as an Analytical Expression for Optimization Purpose

This post is not really about a specific question but rather a topic I am curious about to know more. We know that when it comes to integrate machine/statistical learning with optimization for the ...
chupa_kabra's user avatar
  • 1,485
14 votes
1 answer
771 views

Estimation of the size of Branch-and-Bound trees using ML

A short background: A paper [1] published in 2006 intends to show that the time needed to solve mixed-integer programming problems by branch and bound can be roughly predicted early in the solution ...
Oguz Toragay's user avatar
  • 8,652
19 votes
3 answers
436 views

AI gets a lot of attention these days. Does constraint optimization get more attention, too? Why (not)?

Looking at the news as well as at content of tech conferences, I think it is fair to say that AI is getting a lot of attention -- one might even call it an AI hype (like in the 80's). Plenty of ...
Geoffrey De Smet's user avatar
41 votes
11 answers
7k views

Machine learning and operations research projects

Can someone give me some suggestions for projects that use both machine learning/deep learning and operations research to solve business problems? Background: I am a student in OR and I am learning ...
Antarctica's user avatar
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25 votes
5 answers
2k views

Examples of machine learning applied to operations research?

Can someone give me a few examples, if they exist, of problems in operations research that could be solved using machine learning. I am aware that machine learning examples are data-driven and do not ...
klaus's user avatar
  • 353
24 votes
4 answers
1k views

What are the tradeoffs between "exact" and Reinforcement Learning methods for solving optimization problems

Exact methods, e.g., models that utilize an MIP approach with a specified objective and constraints, have advantages like the following: Using off the shelf solvers Optimality gap provability ...
fhk's user avatar
  • 1,069
18 votes
3 answers
2k views

What is the connection of Operations Research and Reinforcement Learning?

I know that Markov Chains and Markov Decision Processes have been studied in the OR community too. But, I was wondering what is the relationship of Operations Research (OR) and Reinforcement Learning (...
Afshin Oroojlooy's user avatar